Data Center GPU Market Size, Share & Industry Trends Analysis Report by Deployment Type (Cloud, On-premise), Function (Training, Inference), End User (Cloud Service Providers, Enterprises, Government) and Region ( North America, Europe, Asia Pacific, ROW) - Global Forecast to 2028
Updated on : Oct 22, 2024
Data Center GPU Market Size & Growth
The global data center GPU market size was valued at USD 14.3 billion in 2023 and is estimated to reach USD 63.0 billion by 2028, growing at a CAGR of 34.6% during the forecast period from 2023 to 2028
The growth of the data center GPU market is governed by rising focus on parallel computing in artificial intelligence (AI) data centers, growing use of deep learning technology in big data analytics, increasing data traffic and need for high computing power.
Data Center GPU Market Forecast to 2028
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Data Center GPU Market Trends
Drivers: Rising adoption of data center GPUs in enterprises
On- premise technology investments, are being made by most of the organizations, so for cloud adoption, operating in a hybrid architecture is necessary. Hence, cloud providers are selected by organizations for implementing hybrid cloud architecture without further investments in on-premise hardware and software in order to achieve their business goals.
Amazon Web Services (US) entered in to a partnership with on-premise platform providers, such as Intel (US), Microsoft (US), and VMware (US), for developing hybrid capabilities across storage, networking, security, and management tools; and application deployments for making the integration of cloud services easy.
Enterprises can run their existing applications with high performance on Amazon Web Services due to these hybrid capabilities. Growth opportunities are being sought by key tech giants in enterprise inference applications by installing enterprise solutions in data centers. NVIDIA’S Apache Spark 3.0 is the first release of Spark that offers fully integrated and seamless GPU acceleration for analytics and AI workloads. The breakthrough performance of GPUs empowers enterprises and researchers to train bigger models more frequently, ultimately realising the value of big data with the power of AI.
Restraint: High costs associated with data center GPUs
Graphics Processing Units (GPUs) have been originally designed for the purpose of rendering graphics and images, however they have emerged as powerful accelerators for data analytics tasks, in recent years,. Even though GPUs offer better speed and performance, they are more expensive compared to CPUs. . This is particularly true in the case of high-end GPUs with specialized architectures and features. On the other hand, CPUs, are typically less expensive, which makes them a more budget-friendly option for some data analytics projects.
High-performance data center GPUs can be expensive, both in terms of initial acquisition costs and ongoing operational expenses related to power and cooling.
Operational costs associated with the data center GPUs include acquisition costs, implementation costs of the GPUs, network integration costs, costs for system management, maintenance costs, costs for troubleshooting, and repair and training costs. network integration costs, costs for system management, maintenance costs, costs for troubleshooting, and repair and training costs. Integrating GPUs into existing data center infrastructure may require upgrades or modifications to support the additional power and cooling requirements. This can add to the initial investment costs.
GPUs generate significant heat when operating at full capacity. Data centers require efficient cooling systems to maintain optimal operating temperatures. Cooling can account for a significant portion of operational expenses, including electricity costs and maintenance. Software licensing costs for GPU-accelerated applications and frameworks can add to operational expenses. Enterprises may need to invest in specialized software or pay for GPU usage in cloud services.
Opportunity: Growing potential of GPUs in the healthcare sector
Aging is associated with progressive deterioration in the structure and function of organs. With the growth in the geriatric population, the incidence of various age-related diseases is expected to increase worldwide. Governments in several countries are increasingly focusing on adopting novel technologies to counter this and efficiently handle the growing burden on their respective healthcare systems. In scenarios like these, the introduction of Graphics Processing Units (GPUs) could potentially revolutionize the game.
GPUs prove to be particularly well-suited for situations where the intricacy of tasks has historically hindered the swift and accurate generation of insights. Their remarkable capacity for parallelizing tasks allows for an improvement of a magnitude previously unattainable, far surpassing the incremental gains achieved by even the most high-powered traditional computing systems. With the emergence of more recent enterprise GPU chipsets, exemplified by the likes of the NVIDIA Ampere A100, the potential is unlocked to possess up to 70 times more cores at a fraction of the cost when compared to equivalent Central Processing Units (CPUs). Technological advancements have lowered the barriers to harnessing GPUs, providing healthcare companies with a valuable opportunity to accelerate tasks and harness data as never before.
Challenge: Security concerns associated with data center GPUs
Since data centers store sensitive information, including financial transactions, personnel records, and other corporate data that are both crucial and confidential, data center network connections are required to be reliable, safe, and, in many cases, encrypted to avoid costly breaches and data losses.
Data Center GPUs, like any other hardware components in a data center environment, come with specific security concerns that need to be addressed to protect sensitive data and maintain the integrity of the infrastructure. Unauthorized physical access to Data Center GPUs can lead to tampering, theft, or replacement with compromised hardware. Data centers must implement strict access controls, surveillance, and physical security measures to prevent unauthorized individuals from accessing the hardware.
For example, recently, ESEA, a paid subscription service that offers competitive matchmaking, league play and cheat prevention, admitted that their users' graphic cards had been hijacked to mine Bitcoin virtual currency. A malicious entity has hidden aa bitcoin miner in ESEA (a video game service) software. This miner used the GPUs in users’ machines to earn cryptocurrency without their knowledge. The miner overheated and harmed the machines by overloading the GPUs.
GPUs have firmware that can potentially be targeted by malicious actors. Attacks on GPU firmware can lead to data exfiltration or compromised performance. Regular firmware updates and security audits are essential to mitigate this risk.
Data Center GPU Ecosystem
Healthcare segment to exhibit the second-highest growth in terms of data center GPU market for enterprises during the forecast period
The healthcare segment is expected to exhibit the second-highest growth for the data center GPU industry for enterprises during the forecast period. Data center GPUs are currently being used in many aspects of the healthcare industry, including medical imaging and diagnosis, drug discovery and development, clinical data analysis, radiotherapy planning, medical simulation and training, telemedicine and remote diagnostics, and healthcare analytics.
The market for training segment to witness higher growth during the forecast period
The market for training segment is expected to grow a higher CAGR during the forecast period. Training is computationally costly and is best accelerated with GPUs. The time taken to go through all the training samples can be reduced using GPU compared with CPU, even when using a small dataset.
Automotive enterprise segment to exhibit the highest growth in terms of data center GPU market during the forecast period
The data center GPU market for the automotive enterprise segment is expected to exhibit the highest growth during the forecast period. The automotive industry relies on high-performance GPUs to manage graphics processing, including video and imaging, for a range of applications.
Autonomous Driving and ADAS (Advanced Driver Assistance Systems), training and testing of AI models, data processing and analytics, infotainment and user experience, cybersecurity are some of the applications in which GPUs are being leveraged. Most autonomous vehicle manufacturers currently use GPU for their core AI processing. In March 2022, NVIDIA announced its new H100 AI processor, a successor to the A100, used for plotting a self-driving car’s route through traffic. The company also announced working on a new generation of its Hyperion car chip family, which is due in 2026.
Data Center GPU Market - Regional Analysis
Data Center GPU market in Asia Pacific estimated to grow at the fastest rate during the forecast period
The data center GPU market, in Asia Pacific is expected to grow at the highest CAGR during the forecast period. Asia Pacific is the host to a few of the fastest-growing and leading industrialized economies, such as China, Japan, and India in the world. It is witnessing dynamic changes in the adoption of new technologies and advancements in organizations across industries. Increasing adoption of deep learning and NLP technologies for finance, agriculture, marketing, and law applications is also driving the market in this region
Data Center GPU Market by Region
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Top Data Center GPU Companies - Key Market Players
Major vendors in the data center GPU companies include NVIDIA Corporation (NVIDIA) (US), Intel Corporation (Intel) (US), Advanced Micro Devices, Inc (AMD) (US), Samsung Electronics Co., Ltd. (Samsung) (South Korea), Micron Technology, Inc. (Micron) (US), Qualcomm Technologies, Inc. (US), International Business Machines Corporation (IBM) (US), Google Inc. (Google) (US), Microsoft Corporation (Microsoft) (US), Imagination Technologies (UK), Advantech Co., Ltd. (Taiwan), Huawei Technologies Co., Ltd. (Huawei) (China), ZOTAC Technology Ltd. (Hong Kong), Apple Inc. (US), GIGABYTE Technology Co., Ltd. (Taiwan), Arm Ltd. (UK), and Graphcore (UK) are some of the key players in the data center GPU market.
Data Center GPU Market Report Scope
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Report Metric |
Details |
| Estimated Market Size | USD 14.3 billion in 2023 |
| Projected Market Size | USD 63.0 billion by 2028 |
| Data Center GPU Market Growth Rate | CAGR of 34.6% |
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Data Center GPU Market size available for years |
2019—2028 |
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Base year |
2022 |
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Forecast period |
2023—2028 |
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Segments covered |
Deployment Type, Function, End User and Region |
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Geographic regions covered |
North America, Europe, Asia Pacific, and RoW |
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Companies covered |
NVIDIA Corporation (NVIDIA) (US), Intel Corporation (Intel) (US), Advanced Micro Devices, Inc (AMD) (US), Micron Technology, Inc. (Micron) (US), International Business Machines Corporation (IBM) (US), Samsung Electronics Co., Ltd. (Samsung) (South Korea), Qualcomm Technologies, Inc. (US), Google Inc. (Google) (US), Microsoft Corporation (Microsoft) (US), Imagination Technologies (UK), Advantech Co., Ltd. (Taiwan), Huawei Technologies Co., Ltd. (Huawei) (China), ZOTAC Technology Ltd. (Hong Kong), Apple Inc. (US), GIGABYTE Technology Co., Ltd. (Taiwan), Arm Ltd. (UK), and Graphcore (UK). |
Data Center GPU Market Highlights
This research report categorizes the data center GPU market based on deployment type, function, end user, and Region.
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Subsegment |
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By Deployment Type: |
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By Function: |
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By End User: |
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By Region: |
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Recent Developments in Data Center GPU Industry
- In August 2023, NVIDIA Corporation (US) announced NVIDIA OVX servers featuring the new NVIDIA L40S GPU, a powerful, universal data center processor designed to accelerate the most compute-intensive, complex applications, including AI training and inference, 3D designs and visualization, video processing and industrial digitalization with the NVIDIA Omniverse platform.
- In June 2023, Intel Corporation (US) introduced the Intel Arc Pro A60 and Pro A60M as new members of the Intel Arc™ Pro A-series professional range of graphics processing units (GPUs). The new products are a significant step up in performance in the Intel Arc Pro family and are carefully designed for professional workstations users with up to 12GB of video memory (VRAM) and support for four displays with high dynamic range (HDR) and Dolby Vision support.
Key Questions Addressed in the Report :
What will be the dynamics for the adoption of data center GPU market based on the deployment type?
The on-premise segment is projected to dominate the data center GPU market during the the forecast period. Some enterprises and organizations, especially those dealing with sensitive data such as healthcare, finance, and government, may prefer to keep their data on-premises due to concerns about data security, compliance, and regulatory requirements. On-premise solutions offer more control over data and security measures. Enterprises with existing on-premise data centers and infrastructure may choose to continue using on-premise GPU deployments to leverage their existing investments.
Which end user segment will contribute more to the overall market share by 2028?
The enterprises end user segment will contribute the most to the data center GPU market. The market for enterprises end user segment segment is expected to account for largest share of the data center GPU market during the forecast period. Enterprises are increasingly integrating AI and machine learning into their operations, from improving customer experiences to optimizing supply chains. Data center GPUs are essential for training and running AI models, which is driving demand. Enterprises engaged in scientific research, engineering simulations, or other computationally intensive tasks benefit from data center GPUs. These GPUs enhance the performance of high-performance computing (HPC) clusters. Data center GPUs are highly scalable, making them suitable for enterprises that need to expand their computational resources as their businesses grow.
How will technological developments such as big data analytics, artificial intelligence (AI), machine learning change the data center GPU market landscape in the future?
Growth in big data analytics business worldwide is helping drive the market for deep learning, eventually driving the demand for data center accelerators for optimizing data processing in data centers. The evolution of technologies, namely, machine learning and artificial intelligence (AI), has generated the demand for cognitive computing technology across various verticals such as automotive, industrial, and consumer. Rapid developments in the video analytics domain and the increasing adoption of advanced technologies in the security and surveillance industry have resulted in the development of high-performance AI-capable processors such as GPUs and TPUs.
Which region is expected to adopt data center GPUs at a fast rate?
Asia Pacific region is expected to adopt data center GPUs at the fastest rate. Developing countries such as India and China are expected to have a high potential for the future growth of the market.
What are the key market dynamics influencing market growth? How will they turn into strengths or weaknesses of companies operating in the market space?
The automotive enterprises segment is witnessing increased adoption of data center GPUs. The automotive industry relies on high-performance GPUs to manage graphics processing, including video and imaging, for a range of applications. Autonomous Driving and ADAS (Advanced Driver Assistance Systems), training and testing of AI models, data processing and analytics, infotainment and user experience, cybersecurity are some of the applications in which GPUs are being leveraged.
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The study involved four major activities in estimating the size for data center GPU market. Exhaustive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across value chains through primary research. The bottom-up approach was employed to estimate the overall market size. After that, market breakdown and data triangulation were used to estimate the market size of segments and subsegments.
Secondary Research
In the secondary research process, various sources were used to identify and collect information important for this study. These include annual reports, press releases & investor presentations of companies, white papers, technology journals, and certified publications, articles by recognized authors, directories, and databases.
Secondary research was mainly used to obtain key information about the supply chain of the industry, the total pool of market players, classification of the market according to industry trends to the bottom-most level, regional markets, and key developments from the market and technology-oriented perspectives.
Primary research was also conducted to identify the segmentation types, key players, competitive landscape, and key market dynamics such as drivers, restraints, opportunities, challenges, and industry trends, along with key strategies adopted by players operating in the data center GPU market. Extensive qualitative and quantitative analyses were performed on the complete market engineering process to list key information and insights throughout the report.
Primary Research
Extensive primary research has been conducted after acquiring knowledge about the data center GPU market scenario through secondary research. Several primary interviews have been conducted with experts from both demand (end users) and supply side (data center GPU providers) across 4 major geographic regions: North America, Europe, Asia Pacific, and RoW. Approximately 80% and 20% of the primary interviews have been conducted from the supply and demand side, respectively. These primary data have been collected through questionnaires, emails, and telephonic interviews.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
In the complete market engineering process, both the top-down and bottom-up approaches were implemented, along with several data triangulation methods, to estimate and validate the size of the data center GPU market and various other dependent submarkets. Key players in the market were identified through secondary research, and their market share in the respective regions was determined through primary and secondary research. This entire research methodology included the study of annual and financial reports of the top players, as well as interviews with experts (such as CEOs, VPs, directors, and marketing executives) for key insights (quantitative and qualitative).
All percentage shares, splits, and breakdowns were determined using secondary sources and verified through primary sources. All the possible parameters that affect the markets covered in this research study were accounted for, viewed in detail, verified through primary research, and analyzed to obtain the final quantitative and qualitative data. This data was consolidated and supplemented with detailed inputs and analysis from MarketsandMarkets and presented in this report.
Data Center GPU Market: Bottom-Up Approach
Data Triangulation
After arriving at the overall market size from the market size estimation process as explained above, the total market has been split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments, market breakdown and data triangulation procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both demand and supply sides. Along with this, the market has been validated using top-down and bottom-up approaches.
Definition
Data center GPUs are powerful accelerators that are deployed alongside CPUs in both cloud and on-premises data center environments. By delivering high-performance parallel processing capabilities, data center GPUs enable key workloads such as AI, analytics, rendering, and simulation/modeling. To support AI, analytics, 3D rendering, and other advanced workloads, GPUs play an expanded role in the data center environment. By augmenting CPUs with powerful parallel processing capabilities, data center GPUs help speed outcomes and accelerate innovation.
Key Stakeholders
- Senior Management
- End User
- Finance/Procurement Department
- R&D Department
Report Objectives
- To describe and forecast the data center GPU market, in terms of value, based on type, function, end user and region, in terms of value.
- To forecast the data center GPU market in terms of volume
- To provide the market size estimation for North America, Europe, Asia Pacific, and the Rest of the World (RoW), along with their respective country-level market sizes, in terms of value
- To provide detailed information regarding the drivers, restraints, opportunities, and challenges influencing the market growth
- To provide a detailed overview of the data center GPU value chain
- To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contributions to the overall market
- To provide information regarding trade data related to the data center GPU market.
- To provide key technology trends and patent analysis related to the data center GPU market.
- To analyze the opportunities in the market for various stakeholders by identifying high-growth segments and provide a detailed competitive landscape of the data center GPU market.
- To strategically profile key players and comprehensively analyze their market rankings and core competencies2.
- To benchmark the market players using the proprietary company evaluation matrix framework, which analyzes the market players on various parameters within the broad categories of market rankings/shares and product portfolios.
- To analyze competitive developments such as contracts, agreements, expansions, acquisitions, product launches, collaborations, and partnerships, along with research and development (R&D), in the data center GPU market.
Available Customizations
With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product matrix that gives a detailed comparison of the product portfolio of each company
Company Information
- Detailed analysis and profiling of additional market players (up to 7)
Growth opportunities and latent adjacency in Data Center GPU Market